Post-filtering of named entities with machine learning

ABSTRACT

A method for identifying errors associated with named entity recognition includes recognizing a candidate named entity within a text and extracting a chunk from the text containing the candidate named entity. The method further includes creating a feature vector associated with the chunk and analyzing the feature vector for an indication of an error associated with the candidate named entity. The method also includes correcting the error associated with the candidate named entity.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication No. 62/674,312, filed May 21, 2018, the entire content ofwhich is hereby incorporated by reference in its entirety.

BACKGROUND

When processing and reviewing documents on an electronic device, thedocuments may be scanned into document images or stored as a text. Wherenecessary, the text contained in document images may be recognized by anoptical character recognition (OCR) system. Recognizing the text of thedocument image may enable the computing system to perform furtheranalysis. For example, some types of documents contain named entitiesthat are important to understanding the document. After recognizing thetext, some document processing systems also attempt to identify namedentities contained within the text of the document.

SUMMARY

The present disclosure presents new and innovative systems and methodsfor identifying errors associated with named entity recognition in atext. The following are example embodiments of such systems and methods.Although discussed individually, it should be understood that each ofthe below example embodiments may be combined with one or moreadditional example embodiments, and each such combined embodiment isherewith also disclosed.

In an example, a computer-implemented method is provided comprisingrecognizing a candidate named entity within a text, extracting a chunkfrom the text, wherein the chunk contains the candidate named entity,and creating a feature vector including a feature of the chunk. In someexamples, the method may further comprise analyzing the feature vectorwith a classifier to identify an error associated with the candidatenamed entity and correcting the error associated with the candidatenamed entity. In another example, the method may further comprisestoring a document image in a memory and recognizing the text from thedocument image. In a further example, the text is recognized from thedocument image by performing optical character recognition on thedocument image. In a still further example, the error associated withthe candidate named entity is that the candidate named entity is not anamed entity and correcting the error associated with the candidatenamed entity includes removing the candidate named entity as a potentialnamed entity in the text. In another example, the classifier analyzesthe feature vector using a first machine learning model. In a furtherexample, the first machine learning model includes one or more of arecurrent neural network, a convolutional neural network, a conditionalrandom field model, and a Markov model. In a still further example, themethod further comprises receiving a labeled training chunk comprising(i) a candidate training named entity, (ii) a training chunk associatedwith the candidate training named entity, and (iii) a labeling outputindicating whether the candidate training named entity is a namedentity. In another example, the method further comprises creating atraining feature vector, wherein the training feature vector includes afeature of the training chunk, analyzing the training feature vectorusing the first machine learning model to create a machine learningtraining output comprising an indication of whether the first machinelearning model identified an error associated with the candidatetraining named entity, comparing the machine learning training outputwith the labeling output to create a training output comparison thatidentifies one or more errors in the training output, and updating oneor more parameters of the first machine learning model based on thetraining output comparison. In a further example, the classifier isinitially configured to identify errors associated with candidate namedentities recognized from a first document type and updating one or moreparameters of the first machine learning model enables the classifier toidentify errors associated with candidate named entities recognized froma second document type. In a still further example, the candidate namedentity is recognized using a second machine learning model. In anotherexample, the feature vector includes one or more of a named entity labelassociated with the candidate named entity, a recognition accuracyprediction of the candidate named entity, a distance measure between thechunk and a previous chunk and/or a subsequent chunk, an embeddingvector associated with the chunk, semantics of the chunk, and asimilarity of the candidate named entity contained within the chunk anda named entity and/or a candidate named entity contained within apreviously-identified chunk. In a further example, removing thecandidate named entity improves the accuracy of named entitiesrecognized within the text. In a still further example, the steps of themethod are performed on a plurality of candidate named entitiesrecognized within the text.

In an example, a system is provided comprising a classifier, aprocessor, and a memory. The memory contains instructions that, whenexecuted by the processor, cause the processor to receive a chunk from atext, wherein the chunk contains a candidate named entity recognizedwithin the text, create a feature vector including a feature of thechunk, analyze the feature vector with the classifier to identify anerror associated with the candidate named entity, and correct the errorassociated with the candidate named entity. In another example, theerror associated with the candidate named entity is that the candidatenamed entity is not a named entity and correcting the error associatedwith the candidate named entity includes removing the candidate namedentity as a potential named entity in the text. In a further example,the classifier analyzes the feature vector using a first machinelearning model. In a still further example, the classifier includes oneor more of a recurrent neural network, a convolutional neural network, aconditional random field model, and a Markov model. In another example,the memory contains further instructions that, when executed by theprocessor, cause the processor to receive a labeled training chunkcomprising (i) a candidate training named entity, (ii) a training chunkassociated with the candidate training named entity, and (iii) alabeling output indicating whether the candidate training named entityis a named entity, create a training feature vector, wherein thetraining feature vector includes a feature of the training chunk, andanalyze the training feature vector using the first machine learningmodel to create a machine learning training output comprising anindication of whether the first machine learning model identified anerror associated with the candidate training named entity. The memorymay contain further instructions that, when executed by the processor,cause the processor to compare the machine learning training output withthe labeling output to create a training output comparison thatidentifies one or more errors in the training output and update one ormore parameters of the first machine learning model based on thetraining output comparison. In a further example, the classifier isinitially configured to identify errors associated with candidate namedentities recognized from a first document type and updating one or moreparameters of the first machine learning model enables the classifier toidentify errors associated with candidate named entities recognized froma second document type. In a still further example, the system furthercomprises an initial processing system configured to receive a documentimage, perform OCR on the document image to recognize a text of thedocument image and create an OCR document, and recognize a candidatenamed entity within the text. In another example, the initial processingsystem further comprises a chunk extractor configured to extract thechunk from the text. In a further example, the initial processing systemincludes a second machine learning model configured to recognize thecandidate named entity within the text. In a still further example, thefeature vector includes one or more of a named entity label associatedwith the candidate named entity, a recognition accuracy prediction ofthe candidate named entity, a distance measure between the chunk and aprevious chunk and/or a subsequent chunk, an embedding vector associatedwith the chunk, semantics of the chunk, and a similarity of thecandidate named entity contained within the chunk and a named entityand/or a candidate named entity contained within a previously-identifiedchunk. In a further example, the system is configured to receive andprocess a plurality of chunks, each containing a candidate named entityrecognized within the text.

In another example, a computer readable medium is provided, storinginstructions which, when executed by one or more processors, cause theone or more processors to recognize a candidate named entity within atext, extract a chunk from the text, wherein the chunk contains thecandidate named entity, create a feature vector including a feature ofthe chunk, analyze the feature vector with a classifier to identify anerror associated with the candidate named entity, and correct the errorassociated with the candidate named entity. In a further example, thecomputer readable medium stores further instructions which, whenexecuted by the one or more processors, cause the one or more processorsto store a document image in a memory and recognize the text from thedocument image. In a still further example, the computer readable mediumstores further instructions which, when executed by the one or moreprocessors, cause the one or more processors to recognize the text fromthe document by performing optical character recognition (OCR) on thedocument image. In another example, the error associated with thecandidate named entity is that the candidate named entity is not a namedentity and correcting the error associated with the candidate namedentity includes removing the candidate named entity as a potential namedentity in the text. In a further example, the computer readable mediumstores further instructions which, when executed by the one or moreprocessors, cause the one or more processors to analyze the featurevector with the classifier using a first machine learning model. In astill further example, the first machine learning model includes one ormore of a recurrent neural network, a convolutional neural network, aconditional random field model, and a Markov model. In another example,the computer readable medium stores further instructions which, whenexecuted by the one or more processors, cause the one or more processorsto receive a labeled training chunk comprising (i) a candidate trainingnamed entity, (ii) a training chunk associated with the candidatetraining named entity, and (iii) a labeling output indicating whetherthe candidate training named entity is a named entity and create atraining feature vector, wherein the training feature vector includes afeature of the training chunk. In a further example, the computerreadable medium stores further instructions which, when executed by theone or more processors, cause the one or more processors to analyze thetraining feature vector using the first machine learning model to createa machine learning training output comprising an indication of whetherthe first machine learning model identified an error associated with thecandidate training named entity, compare the machine learning trainingoutput with the labeling output to create a training output comparisonthat identifies one or more errors in the training output, and updateone or more parameters of the first machine learning model based on thetraining output comparison. In a still further example, the classifieris initially configured to identify errors associated with candidatenamed entities recognized from a first document type and updating one ormore parameters of the first machine learning model enables theclassifier to identify errors associated with candidate named entitiesrecognized from a second document type. In another example, the computerreadable medium stores further instructions which, when executed by theone or more processors, cause the one or more processors to recognizethe candidate named entity using a second machine learning model. In afurther example, the feature vector includes one or more of a namedentity label associated with the candidate named entity, a recognitionaccuracy prediction of the candidate named entity, a distance measurebetween the chunk and a previous chunk and/or a subsequent chunk, anembedding vector associated with the chunk, semantics of the chunk, anda similarity of the candidate named entity contained within the chunkand a named entity and/or a candidate named entity contained within apreviously-identified chunk. In a still further example, removing thecandidate named entity improves the accuracy of named entitiesrecognized within the text. In another example, the computer readablemedium stores further instructions which, when executed by the one ormore processors, cause the one or more processors to recognize andprocess a plurality of candidate named entities.

The features and advantages described herein are not all-inclusive and,in particular, many additional features and advantages will be apparentto one of ordinary skill in the art in view of the figures anddescription. Moreover, it should be noted that the language used in thespecification has been principally selected for readability andinstructional purposes, and not to limit the scope of the inventivesubject matter.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a document processing system according to an exampleembodiment of the present disclosure.

FIG. 2 illustrates a plurality of feature vectors according to anexample embodiment of the present disclosure.

FIG. 3 illustrates a flow chart of an example method according to anexample embodiment of the present disclosure.

FIGS. 4A to 4E illustrate an example named entity recognition procedureaccording to an example embodiment of the present disclosure.

FIG. 5 illustrates a flow diagram of an example method according to anexample embodiment of the present disclosure.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

One growing area of application of automated document processing is theautomated analysis of legal documents. For example, automated tools,such as those from Leverton GmbH, can be used to automate the process ofreviewing large numbers of contracts, leases, title deeds, and otherlegal or financial documents during a due diligence process. To automatethe analysis of these documents, an important step is to identify namedentities in the legal documents. Named entities are textual elements(e.g., words, phrases, or strings) contained within the text of adocument identifying information relevant to understanding orinterpreting the document. Examples of named entities include propernouns, including the names of persons or legal entities involved in thetransaction embodied in a legal document, such as a party to anagreement, e.g., a landlord, a tenant, a buyer, a seller, a guarantor,mortgagor, mortgagee, lender, guarantor, a licensor, or a licensee.Named entities may also include other information relevant tounderstanding the transaction embodied in the document, such asaddresses or locations, real estate properties, buildings, numbers,dates, and activities. Other examples of named entities may include thename of products or services purchased under an agreement, activities tobe performed under an agreement, defined terms in an agreement, andeffective dates of an agreement. The types of named entities present ina document may depend on the type of document.

For example, in analyzing a purchase agreement, it is often important toidentify who the buyer and seller are. This may enable a better duediligence analysis of which companies a buyer is contracting with andthus exposed to. Another example is in the analysis of leases, where itis often important to identify a landlord and a tenant. Identifying andanalyzing these individuals is often necessary to properly understandthe scope of a portfolio of leases, as well as the reliability of thecash flow associated with the leases. Of course the named entityrecognition problem also exists in other application areas, e.g., theanalysis of financial documents, other agreements, and news articles. Infact, the named entity problem may also exist in application areasoutside of document analysis. Named entity recognition may be used tobetter understand any sequence of words, e.g., a transcribed series ofwords extracted from an audio recording of spoken words.

When recognizing named entities, many named entity recognition (NER)systems utilize one or more heuristics developed by system creators. Forexample, an NER system may identify sequences of two capitalized wordsas named entities because most names are capitalized. State of the artand NER systems may also utilize a machine learning model to a identifynamed entities in a document. For example, a machine learning model maybe trained to identify one or more named entities within the document.These models may be trained using a series of texts wherein namedentities are sparsely distributed, as they typically are in legal andother documents. However, such heuristics and models can often falselyidentify elements of the text as a named entity because the heuristicsare static once created, especially if named entities are sparselydistributed throughout a document. To correct these false positives,some NER systems generate a prediction confidence measurement that maybe based on the strength of a number of heuristics. Such systems maythen filter identified named entities and remove entities with a lowprediction probability.

However, filtering identified named entities in this manner ignorescontextual information available in the document and in other namedentities. For example, a candidate named entity identified far away in adocument from similar named entities may suggest that the candidatenamed entity was incorrectly identified in a type of document wherenamed entities typically occur in groups. In other examples, a candidatenamed entity identified near similar named entities may suggest that thecandidate named entity was incorrectly identified in a type of documentwhere named entities typically occur farther apart. Incorporating suchcontextual information with heuristics is difficult at a large scalebecause contextual relationships are complex when more than two labelsare considered. Further, there may be other types of contextualinformation that are not obvious to system creators.

Additionally, different document types may utilize different heuristicsand may have different kinds of pertinent contextual information. Forexample, a legal document such as a lease may include named entities inthe text of the lease, whereas a financial statement may include namedentities in a table preceding the text of the document. These heuristicscan even change between documents of the same type. For example, a largecommercial lease may include an extensive definitions section thatidentifies the named entities whereas a smaller residential lease maynot contain a definitions section and may simply define the namedentities within the agreement provisions. Accordingly, systems that relysolely on heuristics may have to be extensively redeveloped in order toproperly analyze documents of different types.

One innovative procedure, described in the present disclosure thatsolves both of these problems is to use a machine learning model toidentify falsely-identified candidate named entities. One approach todoing this involves extracting chunks of text that include candidatenamed entities and creating a feature vector that corresponds to eachchunk. These feature vectors may include aspects of the candidate namedentities and of their relationship with preceding or subsequentcandidate named entities. For example, a feature vector may include alabel indicating the type of named entity for the candidate namedentity, as well as its distance to the preceding named entity. Thesefeature vectors may then be analyzed by a machine learning model toidentify false positives. To train the machine learning model, trainingchunks may be created that are labeled to indicate whether theycorrespond to a correctly-identified named entities. The model may thenanalyze training feature vectors corresponding to these training chunksand the model may be adjusted to better classify candidate namedentities as correctly or incorrectly identified. Because the model isconfigured to be trained and updated automatically, rather than manuallyupdated with new heuristics, such a system is also significantly easierto update for new types of documents. Further, because the system isconfigured to work with feature vectors, which may include manydifferent types of features, the model is able to integrate new featuresthat may be relevant to one document type but not to another.

FIG. 1 depicts a document processing system 100 according to an exampleembodiment of the present disclosure. The document processing system 100includes a document 102, an initial processing system 104, and apost-processing system 132. The initial processing system 104 includesan optical character recognizer 106, a named entity recognizer 110, achunk extractor 118, a CPU 128 and a memory 130. The optical characterrecognizer 106 further includes a text 108. The named entity recognizer110 includes a machine learning model 112 and candidate named entities114, 116. The chunk extractor 118 includes chunks 120, 124 that eachinclude a candidate named entity 114, 116. The initial processing system104 is connected to the post-processing system 132, which includes afeature vector creator 134, a classifier 140, a CPU 146, and a memory148. The feature vector creator 134 stores feature vectors 136, 138 inan associated memory 130 and the classifier 140 includes a machinelearning model 142 and a named entity 144.

The initial processing system 104 may be configured to receive adocument 102 and recognize text within the document 102 to create a text108. The document 102 may be stored on the memory 130 after the document102 is received by the initial processing system 104 before the text 108is recognized. The document 102 may be received from a document serverconfigured to store multiple documents. The document 102 may be adocument image, such as a scanned image of a paper document. In someimplementations, if the document 102 is a document image, the initialprocessing system 104 may recognize the text using an optical characterrecognizer 106. The optical character recognizer 106 may be configuredto perform optical character recognition on the document image torecognize the text 108 in the document 102. In other implementations,the document 102 may already have recognized and/or searchable textrecognized (e.g., a word document or a PDF with recognized text). Insuch a case, the initial processing system 104 may not be required torecognize the text 108 and may instead continue processing the document102 and the text 108.

The document 102 may be a particular document type. For example, thedocument 102 may be a lease agreement, a purchase sale agreement, atitle insurance document, a certificate of insurance, a mortgageagreement, a loan agreement, a credit agreement, an employment contract,an invoice, a financial document, and an article. Although depicted inthe singular, in some embodiments the initial processing system 104 maybe configured to receive and process more than one document 102 at atime. For example, the initial processing system 104 may be configuredto receive multiple documents of the same type (e.g., residentialleases) or may be configured to receive multiple documents of multipletypes (e.g., residential leases and commercial leases).

The named entity recognizer 110 may be configured to recognize one ormore candidate named entities 114, 116 in the text 108. The candidatenamed entities 114, 116 may include one or more pieces of informationthat may be important to understanding the text 108, such as persons,organizations, locations, times, dates, quantities, monetary amounts,actions that must be performed, or other items of information. Forexample, the candidate named entities 114, 116 may include one or moreof a landlord, a tenant, a buyer, a seller, a party to an agreement, anentity important to the document, and a defined term in a contract. Thetypes of entities identified as candidate named entities 114, 116 maydiffer based on the document type corresponding to the document 102. Forexample, contact information for individuals other than a contractsignatory may not be important to a lease contract but may be veryimportant to business procurement contracts. Thus, when analyzing a text108 deriving from a lease contract, the named entity recognizer 110 maynot recognize candidate named entities 114, 116 for non-signatoryindividuals. However, when analyzing a text 108 deriving from a businessprocurement contract, the named entity recognizer 110 may be configuredto recognize candidate named entities 114, 116 for non-signatoryindividuals.

The named entity recognizer 110 may be configured to recognize candidatenamed entities 114, 116 using heuristics, such as by identifying twoadjacent capitalized words as a named entity. These heuristics may beprovided by one or more programmers associated with initializing thesystem. Alternatively, the named entity recognizer 110 may be configuredto recognize the candidate named entities using a machine learning model112. The machine learning model 112 may be a neural network, such as arecurrent neural network or a convolutional neural network or anothertype of machine learning model, such as a conditional random field modelor a Markov model. The named entity recognizer 110 may also beconfigured to use a combination of heuristics and a machine learningmodel 112 to recognize the candidate named entities 114, 116. Whenrecognizing named entities, the named entity recognizer 110 may alsogenerate an accuracy measurement that indicates a confidence levelassociated with the recognition of a candidate named entity 114, 116.For example, the accuracy measurement may be a measure of how well thecandidate named entity 114, 116 complies with the combination ofheuristics and the machine learning model 112.

As described above, different named entities may be important fordifferent document types. To account for this, the named entityrecognizer 110 may have a different set of heuristics and/or machinelearning models 112 for different document types. The named entityrecognizer may be configured to identify a document type for thedocument 102 and the text 108 and switch between heuristics and machinelearning models 112 based on the document type. For example, a user mayprovide the document type or the named entity recognizer 110 maydetermine the document type based on metadata or other informationassociated with the document 102 and text 108, such as the title of thedocument 102 or a document type metadata field. In some embodiments,because of inherent errors with the set of heuristics and/or the machinelearning model 112, one or more of the candidate named entities 114, 116may not correspond to named entities 144 important to the document.

The chunk extractor 118 may be configured to extract one or more chunks120, 124 from the text 108. Each of the chunks 120, 124 may contain oneor more candidate named entities 114, 116. The chunks 120, 124 may alsocontain portions of the text surrounding the candidate named entities114, 116. For example, the chunk extractor 118 may be configured toextract a chunk 120, 124 that includes the sentence containing thecandidate named entity 114, 116. The chunk extractor 118 may also beconfigured to extract the paragraph containing the candidate namedentity, or any other subset of the text 108. Further, the chunkextractor 118 may be configured to extract a certain number of words(e.g., 10 words) before and after the candidate named entity 114, 116 ora certain number of characters (e.g., 20 characters) before and afterthe candidate named entity 114, 116.

In some embodiments, the chunks 120, 124 may be extracted usingheuristic rules based on the candidate named entities 114, 116identified by the named entity recognizer 112 (e.g., by the machinelearning model 112). In some embodiments, the named entity recognizer110 may apply a series of labels to words that indicate a prediction asto whether the words indicate the beginning, middle, and end of acandidate named entity 114, 116. For example, the named entityrecognizer 110 may apply labels such as “Landlord-Begin,”“Landlord-Inside,” and “Landlord-End” corresponding to the beginning,middle and end of a candidate named entity (e.g., the first name, middleinitial, and last name of the landlord). Similar labels may also be usedfor a tenant. The chunk extractor 118 may then extract the chunks 120,124 based on a series of rules that use the labels provided by the namedentity recognizer 110. For example, if a sequence of labels includes aword labeled “Landlord-Begin” followed by a word labeled “Landlord-End,”the chunk extractor 118 may create a chunk 120, 124 that contains thewords associated with the “Landlord-Begin” and “Landlord-End.” Inanother example, a sequence of labels may include two consecutive“Landlord-Begin” labels and the chunk extractor 118 may create twochunks, each containing the words associated with one of the“Landlord-Begin” labels.

The CPU 128 and the memory 130 may implement one or more aspects of theinitial processing system 104, such as the optical character recognizer106, the named entity recognizer 110, and the chunk extractor 118. Forexample, the memory 130 may store instructions which, when executed bythe CPU 128 may perform one or more of the operational features of theinitial processing system 104. Additionally, one or more of the opticalcharacter recognizer 106, named entity recognizer 110, and chunkextractor 118 may be implemented as a single software module or process.For example, a single software module or process may implement all threeof the optical character recognizer 106, named entity recognizer 110,and chunk extractor 118. In another example, a single software module orprocess may implement the named entity recognizer 110 and chunkextractor 118.

The post-processing system 132 may be configured to receive the chunks120, 124 for further processing to identify the candidate named entities114, 116 that do not correspond to named entities 144 important to thedocument. The feature vector creator 134 may be configured to receivethe chunks 120, 124 and create feature vectors 136, 138 associated withthe chunks 120, 124. For example, feature vector 136 may be associatedwith chunk 120 and feature vector 138 may be associated with chunk 124.As described below, the feature vectors 136, 138 may contain one or morefeatures associated with the chunks 120, 124 and the candidate namedentities 114, 116.

The classifier 140 may be configured to receive the feature vectors 136,138 and analyze the feature vectors 136, 138 for one or more errorsassociated with the candidate named entities 114, 116. The classifier140 may be further configured to correct the one or more errorsassociated with the candidate named entities 114, 116. The classifier140 may use a machine learning model 142 to analyze the feature vectors136, 138. This machine learning model 142 may include a neural networksuch as a recurrent neural network or a convolutional neural network oranother type of machine learning model such as a conditional randomfield model and a Markov model. The machine learning model 142 maydiffer from the machine learning model 112 of the named entityrecognizer 110. In some configurations, this may be desirable becausethe machine learning model 112 may be configured to recognize candidatenamed entities 114, 116 in a text, and the candidate named entities 114,116 may be sparsely distributed throughout the text. By contrast,because the machine learning model 142 analyzes feature vectors 136, 138associated with candidate named entities 114, 116, the machine learningmodel 142 may not deal with sparsely distributed candidate namedentities 114, 116. Thus, a machine learning model 112 that works wellfor recognizing candidate named entities 114, 116 may, in some cases,not be well-suited to perform the functions of the machine learningmodel 142 in analyzing the feature vectors 136, 138, which areassociated with a dense distribution of identified candidate namedentities 114, 116.

The machine learning model 142 may evaluate one or more features of thefeature vectors 136, 138 to determine whether the corresponding chunks120, 124 contain candidate named entities 114, 116 with associatederrors. For example, the classifier 140 may determine that the candidatenamed entities 114, 116 are not named entities 144. The classifier 140may also determine that the candidate named entities 114, 116 wereidentified as an incorrect type of named entity 144, or include aportion of the text 108 not associated with the named entity 144. Theclassifier 140 may further correct the errors associated with thecandidate named entities 114, 116. For example, if the classifier 140determines that candidate named entity 114 is incorrectly identified asa named entity and that candidate named entity 116 is correctlyidentified as a named entity, the classifier 140 may remove candidatenamed entity 114 as a potential named entity and may further designatecandidate named entity 116 as a named entity 144. The classifier 140 mayalso correct other errors by, for example, correcting a named entitylabel that indicates an incorrect named entity type or by correcting theportion of the text 108 associated with the candidate named entity 114,116.

The CPU 146 and the memory 148 may implement one or more of thepost-processing system 132 features, such as the feature vector creator134 and the classifier 140. For example, the memory 148 may storeinstructions which, when executed by the CPU 146 may perform one or moreof the operational features of the post-processing system 132.

The system 100 may be implemented as one or more computer systems, whichmay include physical systems or virtual machines. For example, theinitial processing system 104 and the post-processing system 132 may beimplemented as separate computer systems. These computer systems may benetworked, for example, the links between system components may beimplemented by a network such as a local area network or the Internet.Alternatively, the initial processing system 104 and the post-processingsystem 132 may be implemented by the same computer system. In suchexamples, the CPU 128 and the CPU 146 may be implemented by the same CPUand the memory 130 and the memory 148 may be implemented by the samememory.

FIG. 2 depicts a plurality of feature vectors 200 according to anexample embodiment of the present disclosure. FIG. 2 depicts featurevectors 206, 220. In some embodiments, as described above, the featurevectors 206, 220 may be used to analyze one or more candidate namedentities 114, 116. For example, the feature vectors 206, 220 may beexample embodiments of the feature vectors 136, 138 of the system 100.In some embodiments, the feature vectors 206, 220 may be associated withone or more chunks 202, 216. As depicted, feature vector 206 isassociated with chunk 202 and feature vector 220 is associated withchunk 216. These associations may indicate that the feature vectors 206,220 were created from features derived from the chunks 202, 216 orrelated to the candidate named entities 204, 206 contained within thechunks 202, 216.

The feature vectors 206, 220 may contain one or more features. Thesefeatures may indicate one or more aspects of the text 108 relating tothe chunks 202, 216 and the candidate named entities 204, 218. Forexample, the feature vector 206 contains a candidate named entity text203, a named entity label 210, an accuracy prediction 212, and adistance between chunks 214 and the feature vector 220 contains acandidate named entity text 217, a named entity label 222, an embeddingvector 224, and a similarity measurement 226. Although the featurevectors 206, 220 are depicted as containing different features, in manyimplementations it may be necessary that the feature vectors 206, 220contain the same features to properly compare between the candidatenamed entities 204, 218 associated with the feature vectors 206, 220. Insuch implementations, the variety of features depicted in the featuresvectors 206, 220 may instead depict the features that may be selected toinclude in the feature vector 206, 220.

As depicted, the feature vectors 206, 220 both include the candidatenamed entity text 204, 218. The candidate named entity text 203, 217 mayinclude a portion of the text 108 that includes the candidate namedentity 204, 218. The feature vectors 210, 222 both include named entitylabels 210, 222. The named entity labels 210, 222 may indicate whichtype of entity the candidate named entity 204, 218 is identified to be.For example, the named entity label 210, 222 may indicate that thecandidate named entity 204, 218 is one or more of a buyer, a seller, alandlord, a tenant, a business, a product, or any other entity importantto the document, as discussed above.

The accuracy prediction 212 may be an indication of the predictedaccuracy of the identification of the candidate named entity 204, 218.For example, a named entity recognizer 110 may generate the accuracyprediction 212 when the named entity recognizer 110 recognizes thecandidate named entities 204, 218 in the text 108 as described above. Alow accuracy prediction may suggest that there is an error associatedwith the candidate named entity 204, 218.

The distance between chunks 214 may indicate a distance measurementbetween the chunk 202 and a prior chunk of a text 108 or a distancemeasurement between the chunk 202 and a subsequent chunk of a text 108.For example, if the chunk 216 is the next chunk following the chunk 202in a text 108, the distance between chunks 214 may be the distance tothe next chunk 216. The distance between chunks 214 may be measured as acount of the characters, words, sentences, and/or paragraphs thatseparate the chunk 202 from the subsequent or prior chunk. The distancebetween chunks 214 may also be measured in sections or subsections of adocument as defined in the headings, or as defined for particulardocument types. In other embodiments, the distance between chunks 214may be measured as a physical distance separating in the document 102.In some examples, a large distance between chunks may indicate thatthere is an error associated with a candidate named entity 204, 218. Forexample, if many candidate named entities are defined near one anotherand thus have a small distance between chunks 214 and one candidatenamed entity 202 has a large distance between chunks 214, the largedistance between chunks 214 may indicate that the candidate named entity202 is associated with an error.

The embedding vector 224 may be a word-to-vector representation of oneor more words contained in the chunk 216 or the candidate named entitytext 217. The embedding vector 224 may include one or more pieces ofinformation regarding the semantics of the candidate named entity text217, including words that are similar to the words contained in the textof the chunk 216. The embedding vector 224 may be provided by a thirdparty and may be stored in a memory 130, 148. The information containedin the embedding vector 224 may be useful for determining whether thereis an error associated with the candidate named entity 218. For example,in lease agreements, typical named entities may include the landlord andthe tenant. However, a particular version of a lease may identify theindividuals as “lessor” and “lessee.” The embedding vector 224 mayindicate that these words are analogous to landlord and tenant and thusenable the proper classification of the candidate named entity 218.

The similarity measurement 226 may include a measure or indication ofthe similarity between the candidate named entity 218 and a previouscandidate named entity in a text 108. For example, if the individual“John Doe” has already been identified as a candidate named entity in atext 108, and the candidate named entity text 217 is identified as “JohnDoe” or “Doe,” the similarity measurement 226 may indicate that thecandidate named entity 218 is similar to the John Doe candidate namedentity. In some embodiments, this indication may suggest there is anerror associated with the candidate named entity 218. For example, incertain agreements, individuals may not be able to accompany more thanone role. Thus an indication that John Doe is acting in two roles maysuggest that Doe is not a new named entity because he is already acandidate named entity. One implementation of the similarity measurement226 may use a binary indicator to identify when the candidate entitytext 217 exactly matches the candidate entity text of a previouscandidate named entity. In another implementation, the similaritymeasurement 226 may calculate the Levenshtein distance between thecandidate entity text 217 and the candidate entity text of a previouscandidate named entity. In a still further implementation, thesimilarity measurement 226 may be calculated by counting the number ofequal character triples (e.g., trigrams) between the candidate entitytext 217 and the candidate entity text of a previous candidate namedentity and normalizing the result.

The similarity measurement 226 may also include a measure or indicationof the similarity between the named entity label 222 of the candidatenamed entity 218 and the named entity label of another candidate namedentity. For example, if there is already a candidate named entity in apurchase agreement associated with the buyer, and the candidate namedentity 218 is identified as the buyer, the similarity measurement 226may indicate that the candidate named entity 218 is similar to the namedentity. In some embodiments, the candidate named entity 218 beingsimilar to a previous named entity may suggest there is an errorassociated with the candidate named entity 218. For example, a purchaseagreement may not be able to have more than one buyer. Thus anindication that there is a candidate named entity 218 for a buyer whenthere is already a candidate named entity buyer may suggest that thecandidate named entity 218 is erroneous. When measuring the similaritybetween the named entity label 222 of the candidate named entity 218 andthe named entity label of a previous candidate named entity, thesimilarity measurement 226 may be calculated using implementations andcalculations similar to those discussed above in connection withmeasuring the similarity between the named entity text 217 and the namedentity text of a previous candidate named entity.

In some embodiments, the feature vectors 206, 220 may be created by afeature vector creator 134. In creating the feature vectors 206, 220,the feature vector creator 134 may analyze text contained within thechunks 202, 216 to ascertain one or more features. The feature vectorcreator 134 may also interact with other systems, such as the namedentity recognizer 110, to gather features associated with the chunks202, 216. The feature vector creator 134 may further interact withexternal systems, such as an embedded vector provider, to gatherfeatures associated with the chunks 202, 216. In some embodiments, thefeature vector creator 134 may also create the feature vectors 206, 220at the same time the chunks 202, 216 are created.

FIG. 3 depicts a flow chart of an example method 300 according to anexample embodiment of the present disclosure. The method 300, whenexecuted, may be used to analyze feature vectors 136, 138, 206, 220associated with one or more candidate named entities 114, 116, 204, 208in order to identify whether the candidate named entities 114, 116, 204,208 are not named entities 144. The method 300 may be implemented on acomputer system, such as the document processing system 100. Forexample, one or more steps of the method 300 may be implemented by theinitial processing system 104 and/or the post-processing system 132. Themethod 300 may also be implemented by a set of instructions stored on acomputer readable medium that, when executed by a processor, cause thecomputer system to perform the method. For example, all or part of themethod 300 may be implemented by the CPUs 128, 146 and the memories 130,148. Although the examples below are described with reference to theflowchart illustrated in FIG. 3, many other methods of performing theacts associated with FIG. 3 may be used. For example, the order of someof the blocks may be changed, certain blocks may be combined with otherblocks, one or more of the blocks may be repeated, and some of theblocks described may be optional.

The method 300 may begin with an initial processing system 104 receivinga document 102 (block 302). The document 102 may be associated with oneor more document types that the initial processing system 104 isconfigured to process, as described above. The initial processing system104 may then perform OCR on the document 102 and generate a text 108(block 304). The initial processing system 104 may perform OCR using anoptical character recognizer 106. After generating the text 108, themethod 300 may proceed with recognizing a candidate named entity 114,116, 204, 218 (block 306). The candidate named entity 114, 116, 204, 218may be recognized using a named entity recognizer 110 and one or both ofa set of heuristics and a machine learning model 112, as describedabove. In some embodiments, the document 102 may already include anassociated text 108. In such embodiments, the method 300 may directlyproceed to recognize named entities (block 306) instead of performingOCR on the document (block 304).

The chunk extractor 118 may then extract a chunk 120, 124, 202, 216 fromthe text 108 (block 308). The chunk 120, 124 may contain the candidatenamed entity 114, 116, 204, 218 and may contain a portion of the text108 surrounding the candidate named entity 114, 116, 204, 218. In someembodiments, the chunk 120, 124 may also contain other aspects of thetext 108, such as the document type associated with the document 102.The feature vector creator 134 may then create a feature vector 136,138, 206, 220 associated with the chunk 120, 124, 202, 216 and thecandidate named entity 114, 116, 204, 218 (block 310). The featurevector 136, 138, 206, 220 may contain one or more features associatedwith the chunk 120, 124, 202, 216 and the candidate named entity 114,116, 204, 218 as described above. In some embodiments, the chunkextractor 118 may extract the chunk 120, 124, 202, 216 and the featurevector creator 134 may create the feature vector 136, 138, 206, 220 inthe same step. For example, the chunk extractor 118 and the featurevector creator 134 may be implemented as a single module or componentthat creates feature vectors 136, 138, 206, 220 at the same time itextracts the chunk 120, 124, 202, 216.

The classifier 140 may then analyze the feature vector 136, 138, 206,220 for one or more indications of an error associated with thecandidate named entity 114, 116, 204, 218. As described above, thefeatures contained within the feature vector 136, 138, 206, 220 that theclassifier 140 analyzes may be different for documents of differentdocument types. In some embodiments, the method 300 may then proceedwith the classifier 140 examining the features from the feature vector136, 138, 206, 220 (block 314). For example, the feature vector 136,138, 206, 220 may include an accuracy prediction 212 that indicates thenamed entity recognizer 110 had a low confidence when the named entityrecognizer 110 recognized the candidate named entity 114, 116, 204, 218.This may suggest that the candidate named entity 114, 116, 204, 218 wasincorrectly identified. In another example, the feature vector 136, 138,206, 220 may indicate that the candidate named entity 114, 116, 204, 218was mentioned in a definitions section of a business procurementcontract. This may suggest that the candidate named entity 114, 116,204, 218 was more likely to be correctly identified.

The classifier 140 may then compare features from the feature vector136, 138, 206, 220 to features of other candidate named entities 114,116, 204, 218 (block 316). For example, some of the features from thefeature vector 136, 138, 206, 220 may include information on othercandidate named entities 114, 116, 204, 218. For example, the distancebetween chunks 214 may include the distance to a previous chunk and thesimilarity measurement 226 may include a similarity between thecandidate named entity 114, 116, 204, 218 and a previous named entity,as described above. As described above, either of these features mayindicate that the candidate named entity 114, 116, 204, 218 wascorrectly or incorrectly identified. In some embodiments, these featuresmay not be included in the feature vector 136, 138, 206, 220 and theclassifier 140 may determine the comparison itself. For example, insteadof receiving a similarity measurement 226, the classifier 140 maycompare the candidate named entity to previously-identified candidatenamed entities and determine the similarity.

The classifier 140 may then determine the presence of an error (block318). As described above, each of the features of the feature vector136, 138, 206, 220 may suggest that the candidate named entity 114, 116,204, 218 is more likely or less likely to be correctly identified as apotential named entity. To determine the presence of an error, theclassifier 140 may combine the suggestions of the features into a finaldetermination of the presence of an error. In some implementations, theclassifier 140 may ignore one or more features and in furtherimplementations the classifier 140 may weight each of the featuresdifferently. For example, the classifier 140 may determine that thesimilarity measurement 226 and the distance between chunks 214 areimportant and that the embedding vector 224 is not important. Theclassifier 140 may then weight the similarity measurement 226 and thedistance between chunks 214 higher than the embedding vector 224. Theclassifier 140 may use a machine learning model 142 to perform thisdetermination and may train the machine learning model 142 to determinethe weights for each of the features, as described in greater detailbelow. Additionally, the classifier 140 may have more than one machinelearning model 142 and may use a different machine learning model 142for documents of different document types. Further, a machine learningmodel 142 may be configured to analyze a second document type bytraining a machine learning model configured to analyze a first documenttype as described below.

All or some of the blocks 314, 316, 318 may be optional. For example,the method 300 may be performed by only examining the features of thefeature vector 136, 138, 206, 220 (block 314) and determining thepresence of an error (block 318). In another example, the method 300 mayonly determine the presence of an error (block 318).

The method 300 may then proceed with evaluating whether the classifier140 has determined the presence of an error (block 320). If theclassifier 140 determines that there is no error, the classifier 140 mayproceed to classify the candidate named entity 114, 116, 204, 218 as anamed entity 144 within the text 108 (block 322). To do this, theclassifier 140 may add the candidate named entity 114, 116, 204, 218 toa list of named entities 144 associated with the text 108 for furtherprocessing.

If the classifier 140 determines that there is an error associated withthe candidate named entity 114, 116, 204, 218, the classifier 140 maycorrect the error (block 324). For example, if the classifier determinesthat the candidate named entity 114, 116, 204, 218 includes an incorrectnamed entity label 210, 222, the classifier 140 may replace the namedentity label 210 with a corrected named entity label. In anotherexample, if the classifier 140 determines that the candidate namedentity 114, 116, 204, 218 includes an incorrect portion of the text 108,the classifier 140 may correct the candidate named entity 114, 116, 204,218 by removing the extraneous portion of the text 108 or bysupplementing the candidate named entity 114, 116, 204, 218 with amissing portion of the text 108. In the preceding two examples, aftercorrecting the candidate named entity 114, 116, 204, 218, the classifier140 may proceed to classify the candidate named entity candidate namedentity 114, 116, 204, 218 as a named entity as discussed above inconnection with block 322. However, in a third example, the classifier140 may determine that the candidate named entity 114, 116, 204, 218 wasincorrectly identified as a potential named entity. To correct thiserror, the classifier may eliminate the candidate named entity 114, 116,204, 218 as a potential named entity. In this or similar examples, themethod 300 may thus finish at block 324.

Although the method 300 is discussed in the context of a singlecandidate named entity 114, 116, 204, 218, the method 300 may beperformed on multiple candidate named entities 112, 114, 204, 218. Forexample, the text 108 may contain multiple candidate named entities 112,114, 204, 218 and the method 300 may be performed on each of thecandidate named entities 112, 114, 204, 218 in order to improve theaccuracy of the recognized named entities. The candidate named entities112, 114, 204, 218 may be analyzed using the method 300 individually orin parallel depending on the implementation.

FIGS. 4A to 4E depict an example named entity recognition procedure 400according to an example embodiment of the present disclosure. In someembodiments, the procedure 400 may be performed according to a methodfor analyzing feature vectors 136, 138, 206, 220 associated with one ormore candidate named entities 114, 116, 204, 218 in order to identifywhether the candidate named entities 114, 116, 204, 218 are namedentities 144, such as the method 300. As described in greater detailbelow, the steps performed in conjunction with the procedure 400 may beperformed by one or more of the initial processing system 104 and thepost-processing system 132.

The procedure 400 may begin in FIG. 4A with the text 402. The text 402may have been extracted from a document 102, which may be a particulardocument type. In this example, the text 402 was extracted from a leaseagreement between an landlord and a tenant. The text 402 may include oneor more named entities, each of which is important to properlyunderstanding the text 402. In this example, John Doe may be a namedentity as the landlord of the agreement and Max Mustermann may be anamed entity as the tenant of the agreement. However, Jim Cane may notbe important to understanding this agreement as he is only needed as apoint of contact in the case of an emergency. Jim Cane may also bementioned in a separate portion of the agreement, such as an emergencycontacts portion of the agreement. Thus, although Jim Cane is named inthe agreement, Jim Cane may not be a named entity of the text 402.

The text 402 may then be processed by a named entity recognizer 110 torecognize the named entities in the text 402. The results from the namedentity recognizer 110 may be depicted in the recognized text 404 of FIG.4B. Here, the bolded text indicates the portion of the text 402 that thenamed entity recognizer 110 recognized as a candidate named entity andthe label is listed after the candidate named entity. The named entityrecognizer 110 has correctly identified John Doe as the landlord in theagreement. The named entity recognizer 110 has also correctly recognizedMax Mustermann as the tenant. However, the named entity recognizer 110has also incorrectly recognized Jim Cane as the landlord.

The recognized text 404 may then be processed by a chunk extractor 118to extract the chunks 406, 408 that contain the candidate named entitiesdepicted in FIG. 4C. Here, the chunk 406 contains both the John Doe andMax Mustermann candidate named entities. In this example, the chunk 406contains multiple candidate named entities, but in other examples theremay be separate chunks for each candidate named entity. Here, however,the chunk extractor 118 included John Doe and Max Mustermann in the samechunk 406 because they were in the same sentence and put Jim Cane in aseparate chunk 408 because Jim Cane was mentioned in another part of thetext 402.

The chunks 406, 408 may then be processed by a feature vector creator134 to create features vectors associated with the candidate namedentities in the chunks 406, 408. These feature vectors are depicted inthe table 410. For example, the feature vector corresponding to John Doeincludes the name John Doe as the candidate named entity, an indicationthat John Doe was recognized as a landlord and an indication that thereis no distance between John Doe and the previous chunk because the chunk406 is the first chunk extracted from the text 402. The feature vectorcorresponding to Max Mustermann includes the name Max Mustermann, anindication that Max Mustermann was recognized as the tenant in theagreement and an indication that the previous chunk (i.e., John Doe) hasa distance of 5 characters. Because the chunk 406 contains two candidatenamed entities, the distance between chunk measurement measures thedistance between the candidate named entities. In anotherimplementation, the distance to previous chunk may instead be set tozero characters to indicate that the candidate named entities John Doeand Max Mustermann are in the same chunk 406. The third feature vectorcorresponding to Jim Cane includes the candidate named entity Jim Cane,the incorrect label indicating he was identified as the landlord underthe agreement, and an indication that the chunk 408 is 5,000 charactersamanner from the previous chunk (i.e., chunk 406) in the text 402.

The feature vectors summarized in the table 410 may then be analyzed bya classifier 140 to identify errors associated with the candidate namedentities. In this example, the classifier 140 may analyze the distanceto previous chunk measurement and notice that the measurement for JimCane has a significantly larger distance measurement than themeasurement corresponding to Max Mustermann. This may indicate that JimCane is mentioned in a different part of the agreement than the othertwo candidate named entities and thus that there is likely an errorassociated with the Jim Cane candidate named entity. The classifier 140may also notice that Jim Cane is identified as the landlord under theagreement, even though John Doe is identified as the landlord earlier inthe document. Based on the parameters established when the machinelearning model 142 associated with the classifier 140 was trained, theclassifier 140 may determine that the earlier identification of John Doeas the landlord is more likely to be correct based off of the structureof the lease agreements analyzed during training. Thus, Jim Cane'ssubsequent identification as landlord may suggest there is an errorassociated with the Jim Cane candidate named entity. Accordingly, theclassifier 140 may determine that Jim Cane was falsely identified as anamed identity and remove Jim Cane as a potential named entity. Theclassifier may also determine that the John Doe and Max Mustermanncandidate named entities were correctly identified and may classify themas named entities in the post-processed text 412 of FIG. 4E. Thus, inthis example, the classifier 140 was able to incorporate the contextualinformation captured in the feature vectors to correctly discern thatJim Cane was incorrectly identified as a named entity.

In some instances, the techniques discussed above may be used to analyzemultiple documents 102 (e.g., a collection of related documents 102).For example, the collection of documents 102 could include a lease andone or more amendments to the lease. The lease may be analyzed by thesystem 100 (e.g., according to the method 300) to identify John Doe asthe landlord and Max Mustermann as the tenant. However, one or more ofthe amendments may also designate a new landlord for a propertyassociated with the lease. For instance, an amendment may change theidentity of the party acting as a landlord as a result of a propertysale (e.g., from “John Doe” to “Apartments Inc.”).

In such instances, the method 300 may be performed on the wholecollection of documents 102. While performing the method 300, the namedentity recognizer 110 may recognize both John Doe and Apartments Inc. ascandidate named entities 114, 116, 204, 218 for the current landlordresponsible under the lease. Each of these candidate named entities 114,116, 204, 218 may then be analyzed similar to the analysis performed oncandidate named entities 114, 116, 204, 218 from a single document 102.For example, the feature vectors 136, 138, 206, and 220 associated witheach of the candidate named entities 114, 116, 204, 218 may include adocument type identifier (e.g., a designation as to whether theoriginating document is a contract, a lease, an amendment to a contractor a lease, or any of the other types of documents discussed herein). Inparticular, the document type for the feature vector 136, 138, 206, 220associated with John Doe may indicate that the candidate named entity114, 116, 204, 218 originated from a lease agreement, and the documenttype for the feature vector 136, 138, 206, 220 associated with theApartments Inc. may indicate that the candidate named entity 114, 116,204, 218 originated from an amendment. Based on these identifications,the machine learning model 142 may identify the Apartments Inc.candidate named entity 114, 116, 204, 218 as the more recent, andtherefore correct, named entity 144 for the current landlord under theagreement. In certain implementations, the features vectors 136, 138,206, 220 may also include other features (e.g., an effective date of theoriginating document 102) to help distinguish between multipleamendments to the same document 102.

FIG. 5 depicts a flow diagram of an example method 500 according to anexample embodiment of the present disclosure. The flow diagram includesa training system 502, a named entity recognizer 504, a chunk extractor506, a labeling system 508, a feature vector creator 510, and aclassifier machine learning model 512. The training system 502 may beconfigured to orchestrate the operation of the method 500 and generateupdated model parameters based on the outputs generated during thetraining, as detailed below. In some embodiments, the training system502 may be a implemented as part of a post-processing 132 or aclassifier 140. The named entity recognizer 504 may be implemented asthe named entity recognizer 110 and may include the machine learningmodel 112. The chunk extractor 506 may be implemented as the chunkextractor 118. The labeling system 508 may be a system that labelscandidate named entities 114, 116, 204, 218 with an indication of thecorrect named entity classification that is desired for each candidatenamed entity 114, 116, 204, 218. The labeling system 508 may include oneor both of a manual labeling system and an automatic labeling system.The feature vector creator 510 may be implemented by the feature vectorcreator 134. The classifier machine learning model 512 may beimplemented as the machine learning model 142 of the classifier 140.

The method 500 may be used to train one or more machine models 512, 142associated with a classifier 140. Training the classifier machinelearning model 512 may improve the accuracy of the classifier machinelearning model 512 at recognizing named entities in a particulardocument type. Alternatively, training the classifier machine learningmodel 512 may enable the classifier machine learning model 512 torecognize named entities in a new document type. For example, theclassifier machine learning model 512 may be initially trained torecognize named entities 144 in business procurement contracts and,after completing the method 500, the classifier machine learning model512 may be able to recognize named entities 144 in commercial leases. Insome embodiments, the method 500 may be performed more than once inorder to train the classifier machine learning model 512. In otherembodiments, the method 500 may only need to be performed once in orderto properly train the classifier machine learning model 512. A machinelearning operator, such as an NER system developer, may determine thenumber of times the method 500 is performed. Alternatively a trainingsystem 502 may determine the number of times the method 500 isperformed. For example, the training system 502 may repeat the method500 until the classifier machine learning model 512 is able to recognizenamed entities in a document type with a particular level of accuracy.

The method 500 may be implemented on a computer system, such as thedocument processing system 100. For example, method 500 may beimplemented in whole or in part by the initial processing system 104and/or the post-processing system 132. The method 500 may also beimplemented by a set of instructions stored on a computer readablemedium that, when executed by a processor, cause the computer system toperform the method. For example, all or part of the method 500 may beimplemented by the CPUs 128, 146 and the memories 130, 148. Although theexamples below are described with reference to the flowchart illustratedin FIG. 5, many other methods of performing the acts associated withFIG. 5 may be used. For example, the order of some of the blocks may bechanged, certain blocks may be combined with other blocks, one or moreof the blocks may be repeated, and some of the blocks described may beoptional.

Additionally, FIG. 5 depicts multiple communications between thetraining system 502, the named entity recognizer 504, the chunkextractor 506, the labeling system 508, the feature vector creator 510,and the classifier machine learning model 512. These communications maybe transmissions between multiple pieces of hardware or may be exchangesbetween different programmatic modules of software. For example, thecommunications may be transmissions over a network between multiplecomputing systems, such as the Internet or a local networkingconnection. These transmissions may occur over a wired or wirelessinterface. Other communications may be exchanges between softwaremodules, performed through an application programming interface (API),or other established communication protocols.

The method 500 may begin with the training system 502 creating atraining text (block 514). The training system 502 may create thetraining text by using an optical character recognizer 106 to extracttext from a document 102. Alternatively, the training system 502 may beconnected to or contain a memory that stores training texts and mayselect one of the training texts for use in training the classifiermachine learning model 512. The training system 502 may create thetraining text based on the purpose for training the classifier machinelearning model 512. For example, if the classifier machine learningmodel 512 is being trained to process a new document type, the trainingsystem 502 may create the training text to include text associated withthe new document type. In another example, if the classifier machinelearning model 512 is being trained to improve its accuracy, thetraining system 502 may create a training text that includesparticularly difficult portions of text.

The named entity recognizer 504 may then recognize candidate namedentities in the training text (block 516). These candidate namedentities may be recognized using a set of heuristics or a machinelearning model 112, as described above. The chunk extractor may thenreceive the candidate named entities and extract training chunks fromthe training text that include the candidate named entities (block 506).

The labeling system 508 may then label the candidate named entities(block 520). In some implementations, the candidate named entities aremanually or automatically labeled with indications of the correct namedentity status, which may include both an indication of whether thecandidate named entity is an actual named entity and the correct namedentity label for the candidate named entity. The training system 502 maythen receive the labeling output (block 522). However, although depictedas occurring during the method 500, in some embodiments the candidatenamed entities may be labeled prior to performing the steps of themethod 500. For example, the candidate named entities may be labeledbeforehand and the labeling output may be stored on a memory containedwithin or connected to the training system 502. Thus, instead ofreceiving the labeling output from the label system 508 at block 522,the training system 502 may instead retrieve the labeling output fromthe memory.

The feature vector creator 510 may then create training feature vectorsbased on the training chunks (block 524). As described above inconnection with the feature vectors 136, 138, 206, 220, the features maycontain one or more pieces of contextual information regarding thetraining chunks. The classifier machine learning model 512 may thenreceive and analyze the training feature vectors (block 526). Theclassifier machine learning model 512 may analyze the training featurevectors in the same manner as discussed above in connection with featurevectors 136, 138, 206, 220. In fact, the classifier machine learningmodel 512 may be trained better if the classifier machine learning model512 analyzes the training feature vectors in the same manner theclassifier machine learning model 512 analyzes feature vectors 136, 138,206, 220 because doing so may produce a better training result and thusfurther improve the accuracy or configuration of the classifier machinelearning model 512. Similarly, the classifier machine learning model 512may then identify errors associated with the training feature vectorsusing techniques similar to those discussed above in connection with thefeature vectors 136, 138, 206, 220 (block 528).

The classifier machine learning model 512 may then generate a machinelearning training output that includes indications of which trainingentities the classifier machine learning model 512 did or did notidentify errors for (block 530). For example, the machine learningoutput may include a list of all of the candidate named entitiesassociated with the training feature vectors and an indication ofwhether the classifier machine learning model 512 identified an errorwith each of the candidate named entities. If the classifier machinelearning model 512 did identify an error associated with a particularcandidate named entity, the machine learning training output may includea summary or description of the error, as well as any corrective actionthe classifier machine learning model 512 may deem adequate to correctthe error. In some embodiments, the classifier machine learning model512 may be configured to format the machine learning training output tobe similar to the formatting of the labeling output.

The training system 502 may then receive the machine learning trainingoutput (block 532) and compare the machine learning training output tothe labeling output (block 534). The training system 502 may compareeach candidate named entity identified in the labeling output determinewhether the classifier machine learning model 512 correctly identifiedthe presence or lack of an error associated with the candidate namedentity. If the classifier machine learning model 512 did correctlyidentify the presence of an error, the training system 502 may thendetermine whether the classifier machine learning model 512 determinethe proper manner to correct the error by comparing an identifiedcorrective action to a labeled corrective action.

Based on the comparison at block 534, the training system 502 may thengenerate updated model parameters (block 536). The updated modelparameters may be generated to improve the accuracy of the classifiermachine learning model 512 by, for example, improving the accuracy ofthe classifier machine learning model 512 at identifying errorsassociated with the candidate named entities or at identifyingcorrective actions in response to identified errors. The updated modelparameters may be generated by, for example, adjusting the weightsassigned to particular features of the training feature vectors. Forexample, if the classifier machine learning model 512 is being trainedon a type of document that has named entities distributed throughout thetext, generating the updated model parameters may include lowering theweight associated with the distance between chunks feature 214. In otherembodiments, generating updated model parameters may also includeconfiguring the feature vector creator 510 to include additionalfeatures in the training feature vectors at block 524. For example, ifthe classifier machine learning model 512 is being trained to process adocument type with inconsistent language, the feature vector creator 510may be configured to include an embedding vector 224 in the trainingfeature vectors. The training system 502 may be configured toautomatically generate the updated model parameters, or may beconfigured to have the updated model parameters generated manually, suchas by a training system operator or document analyst, or may beconfigured to generate the updated model parameters both automaticallyand manually. The classifier machine learning model 512 may then receivethe updated model parameters and be updated to incorporate the updatedmodel parameters (block 538). The method may then repeat again beginningat block 514 to further train the model as discussed above.

All of the disclosed methods and procedures described in this disclosurecan be implemented using one or more computer programs or components.These components may be provided as a series of computer instructions onany conventional computer readable medium or machine readable medium,including volatile and non-volatile memory, such as RAM, ROM, flashmemory, magnetic or optical disks, optical memory, or other storagemedia. The instructions may be provided as software or firmware, and maybe implemented in whole or in part in hardware components such as ASICs,FPGAs, DSPs, or any other similar devices. The instructions may beconfigured to be executed by one or more processors, which whenexecuting the series of computer instructions, performs or facilitatesthe performance of all or part of the disclosed methods and procedures.

It should be understood that various changes and modifications to theexamples described here will be apparent to those skilled in the art.Such changes and modifications can be made without departing from thespirit and scope of the present subject matter and without diminishingits intended advantages. It is therefore intended that such changes andmodifications be covered by the appended claims.

1. A computer-implemented method comprising: recognizing a candidatenamed entity within a text; extracting a chunk from the text, whereinthe chunk contains the candidate named entity; creating a feature vectorincluding a feature of the chunk; analyzing the feature vector with aclassifier to identify an error associated with the candidate namedentity; and correcting the error associated with the candidate namedentity.
 2. The method of claim 1, further comprising: storing a documentimage in a memory; and recognizing the text from the document image. 3.The method of claim 1, wherein the error associated with the candidatenamed entity is that the candidate named entity is not a named entityand correcting the error associated with the candidate named entityincludes removing the candidate named entity as a potential named entityin the text.
 4. The method of claim 1, wherein the classifier analyzesthe feature vector using a first machine learning model.
 5. The methodof claim 4, wherein the first machine learning model includes one ormore of a recurrent neural network, a convolutional neural network, aconditional random field model, and a Markov model.
 6. The method ofclaim 4, further comprising: receiving a labeled training chunkcomprising (i) a candidate training named entity, (ii) a training chunkassociated with the candidate training named entity, and (iii) alabeling output indicating whether the candidate training named entityis a named entity; creating a training feature vector, wherein thetraining feature vector includes a feature of the training chunk;analyzing the training feature vector using the first machine learningmodel to create a machine learning training output comprising anindication of whether the first machine learning model identified anerror associated with the candidate training named entity; comparing themachine learning training output with the labeling output to create atraining output comparison that identifies one or more errors in thetraining output; and updating one or more parameters of the firstmachine learning model based on the training output comparison.
 7. Themethod of claim 6, wherein the classifier is initially configured toidentify errors associated with candidate named entities recognized froma first document type and updating one or more parameters of the firstmachine learning model enables the classifier to identify errorsassociated with candidate named entities recognized from a seconddocument type.
 8. The method of claim 1, wherein the candidate namedentity is recognized using a second machine learning model.
 9. Themethod of claim 1, wherein the feature vector includes one or more of anamed entity label associated with the candidate named entity, arecognition accuracy prediction of the candidate named entity, adistance measure between the chunk and a previous chunk and/or asubsequent chunk, an embedding vector associated with the chunk,semantics of the chunk, and a similarity of the candidate named entitycontained within the chunk and a named entity and/or a candidate namedentity contained within a previously-identified chunk.
 10. The method ofclaim 1, wherein removing the candidate named entity improves theaccuracy of named entities recognized within the text.
 11. The method ofclaim 1, wherein the steps of the method are performed on a plurality ofcandidate named entities recognized within the text.
 12. The method ofclaim 1, wherein the recognizing a candidate named entity within thetext further comprises recognizing a plurality of candidate namedentities from a plurality of texts, and wherein performing the methodcorrects errors associated with at least a subset of the plurality ofcandidate named entities from the plurality of texts.
 13. A systemcomprising: a classifier; a processor; and a memory containinginstructions that, when executed by the processor, cause the processorto: receive a chunk from a text, wherein the chunk contains a candidatenamed entity recognized within the text; create a feature vectorincluding a feature of the chunk; analyze the feature vector with theclassifier to identify an error associated with the candidate namedentity; and correct the error associated with the candidate namedentity.
 14. The system of claim 13, wherein the error associated withthe candidate named entity is that the candidate named entity is not anamed entity and correcting the error associated with the candidatenamed entity includes removing the candidate named entity as a potentialnamed entity in the text.
 15. The system of claim 13, wherein theclassifier analyzes the feature vector using a machine learning model.16. The system of claim 15, wherein the memory contains furtherinstructions that, when executed by the processor, cause the processorto: receive a labeled training chunk comprising (i) a candidate trainingnamed entity, (ii) a training chunk associated with the candidatetraining named entity, and (iii) a labeling output indicating whetherthe candidate training named entity is a named entity; create a trainingfeature vector, wherein the training feature vector includes a featureof the training chunk; analyze the training feature vector using themachine learning model to create a machine learning training outputcomprising an indication of whether the machine learning modelidentified an error associated with the candidate training named entity;compare the machine learning training output with the labeling output tocreate a training output comparison that identifies one or more errorsin the training output; and update one or more parameters of the machinelearning model based on the training output comparison.
 17. The systemof claim 13, further comprising an initial processing system configuredto: receive a document image; perform OCR on the document image torecognize a text of the document image and create an OCR document; andrecognize a candidate named entity within the text.
 18. The system ofclaim 17, wherein the initial processing system further comprises achunk extractor configured to extract the chunk from the text.
 19. Themethod of claim 13, wherein the system is configured to receive andprocess a plurality of chunks, each containing a candidate named entityrecognized within the text.
 20. A computer readable medium storinginstructions which, when executed by one or more processors, cause theone or more processors to: recognize a candidate named entity within atext; extract a chunk from the text, wherein the chunk contains thecandidate named entity; create a feature vector including a feature ofthe chunk; analyze the feature vector with a classifier to identify anerror associated with the candidate named entity; and correct the errorassociated with the candidate named entity.